
We evaluate LDET on two settings of open-world instance segmentation: Cross-category and Cross-dataset.
The Cross-category setting is based on the COCO [1]} dataset, where we split annotated classes into known and unknown classes, train models on known ones, and evaluate detection/segmentation performance on unknown ones.
Since the model can be exposed to a new environment and encounter novel instances, the Cross-dataset setting evaluates models' ability to generalize to new datasets. For this purpose, we adopt either COCO [1]} or Cityscapes [3]} as a training source, with UVO [4]} and Mappilary Vista [5]} being the test datasets, respectively.
In this work, Average Precision (AP) and Average Recall (AR) are employed for performance evaluation. The evaluation is done in a class agnostic way. Unless otherwise specified, AR and AP are computed following the COCO evaluation protocol, , AP or AR at 100 detections at maximum.
